True size eyewear in real time

ABSTRACT

Methods and systems are disclosed for performing operations comprising: receiving an image that includes a depiction of a face of a user; generating a plurality of landmarks of the face based on the received image; removing a set of interfering landmarks from the plurality of landmarks resulting in a remaining set of landmarks of the plurality of landmarks; obtaining a depth map for the face of the user; and computing a real-world scale of the face of the user based on the depth map and the remaining set of landmarks.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/208,159, filed on Mar. 22, 2021, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented-Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example true size estimationsystem, according to example examples.

FIGS. 6-9 are diagrammatic representations of outputs of the true sizeestimation system, in accordance with some examples.

FIGS. 10A and 10B are flowcharts illustrating example operations of themessaging application server, according to examples.

FIG. 11 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 12 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, virtual reality (VR) and augmented reality (AR) systems allowusers to add augmented reality elements, such as augmented realityglasses, to a face of the user depicted in a captured image. To do so,the typical VR/AR systems use specialized techniques that requirecalibration to determine a scale of the user's face in the image. Forexample, such systems instruct the user to place a reference object,such as a credit card, on the user's face or next to the user's face sothat a scale of the face can be computed. The systems can then displayaugmented reality glasses on the user's face based on the calibration.While such systems generally work well, the need to calibrate thesystems places an additional burden on the users and takes away from theenjoyment of the experience. Also, computing the scale by calibratingthe system takes additional time and resources, making such systems lessefficient for general applications.

The disclosed techniques improve the efficiency of using an electronicdevice which implements or otherwise accesses an AR/VR system bycomputing a true or real-world scale of a user's face by combining aselect set of facial landmarks with a depth map of the user's face.Specifically, the disclosed techniques receive an image that includes adepiction of a face of a user and generate a plurality of faciallandmarks based on the received image. Facial landmarks (or landmarks onthe face) can correspond to a predefined region of a person's face suchas a nose, mouth, eyes, etc. The disclosed techniques remove a set ofinterfering facial landmarks from the plurality of facial landmarksresulting in a remaining set of landmarks of the plurality of landmarks.The disclosed techniques obtain a depth map for the face of the user andcompute a real-world scale of the face of the user based on the depthmap and the remaining set of landmarks. The real-world scale of the faceis then used to adjust a size of an augmented reality element, such asaugmented reality glasses (e.g., eyewear) or an augmented reality hat.The real-world scale together with a facial topology is also used toidentify the appropriate position over which to add or display theaugmented reality element on the user's face. As the user moves the facearound in a video, the positioning of the augmented reality elementcontinues to be changed to remain fixed to the identified position ofthe face. The real-world scale of the face continues to be updated asnew images of a video depicting the user's face are received andprocessed in a similar manner.

In this way, the disclosed techniques can apply one or more visualeffects to the user's face in the current image without performing anycalibration operations or pre-capture operations. This improves theoverall experience of the user in using the electronic device andreduces the overall amount of system resources needed to accomplish atask.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications 109 using Applications Program Interfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 114. Specifically, theApplication Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The Application Program Interface (API) server116 exposes various functions supported by the application servers 114,including account registration, login functionality, the sending ofmessages, via the application servers 114, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 118, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208. Scan functionality includes activating andproviding one or more augmented reality experiences on a client device102 when an image is captured by the client device 102. Specifically,the messaging client 104 on the client device 102 can be used toactivate a camera. The camera displays one or more real-time images or avideo to a user along with one or more icons or identifiers of one ormore augmented reality experiences. The user can select a given one ofthe identifiers to launch the corresponding augmented reality experienceor perform a desired image modification (e.g., adding augmented realityeyewear or a hat to a face depicted in an image).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from a external app(s) server 110, a markup-language documentassociated with the small-scale external application and processing sucha document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client messaging clients 104, with the ability to share anitem, status, state, or location in an external resource with one ormore members of a group of users into a chat session. The shared itemmay be an interactive chat card with which members of the chat caninteract, for example, to launch the corresponding external resource,view specific information within the external resource, or take themember of the chat to a specific location or state within the externalresource. Within a given external resource, response messages can besent to users on the messaging client 104. The external resource canselectively include different media items in the responses, based on acurrent context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain augmented reality experiencesand presents identifiers of such experiences in one or more userinterfaces (e.g., as icons over a real-time image or video or asthumbnails or icons in interfaces dedicated for presented identifiers ofaugmented reality experiences). Once an augmented reality experience isselected, one or more images, videos, or augmented reality graphicalelements are retrieved and presented as an overlay on top of the imagesor video captured by the client device 102. In some cases, the camera isswitched to a front-facing view (e.g., the front-facing camera of theclient device 102 is activated in response to activation of a particularaugmented reality experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or augmented reality graphical elementsare retrieved and presented as an overlay on top of the images that arecaptured and displayed by the front-facing camera of the client device102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 106. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions) as well as other payload data (e.g., text,audio, video or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content in according to a common coordinatesystem or frame as the first client device 102.

The augmentation system 208 that can communicate with the client device102 to establish individual or shared AR sessions. The augmentationsystem 208 can also be coupled to the messaging server 118 to establishan electronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access to the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104, and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game, by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes ApplicationProgramming Interfaces (APIs) with functions that can be called orinvoked by the web-based application. In certain examples, the messagingserver 118 includes a JavaScript library that provides a giventhird-party resource access to certain user data of the messaging client104. HTML5 is used as an example technology for programming games, butapplications and resources programmed based on other technologies can beused.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a Web ViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between a external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, two-dimensional avatars ofusers, three-dimensional avatars of users, and avatars with variousavatar characteristics). Avatar characteristics include different waysto customize a look and feel of an avatar, such as different poses,facial features, clothing, and so forth.

The true size estimation system 224 computes a real-world scale of auser's face that is depicted in an image, such as based on faciallandmarks (or subset of facial landmarks) and data received from a depthsensor. For example, the true size estimation system 224 can performobject recognition on the captured video feed to generate a plurality oflandmarks of the face depicted in a received image. In response togenerating the plurality of landmarks, the true size estimation system224 removes a set of interfering landmarks from the plurality oflandmarks resulting in a remaining set of landmarks of the plurality oflandmarks, such as by removing a hair landmark or chin landmark. In somecases, the true size estimation system 224 removes landmarks that have avisibility parameter or stability parameter that is lower than aspecified value. In some cases, the true size estimation system 224sorts the landmarks based on respective visibility and stabilityparameters to generate the remaining set of landmarks. The true sizeestimation system 224 obtains a depth map and then computes a real-wordscale of the face of the user based on the depth map and the remainingset of landmarks. An illustrative implementation of the true sizeestimation system 224 is shown and described in connection with FIG. 5below.

Specifically, the true size estimation system 224 is a component thatcan be accessed by an AR/VR application implemented on the client device102. The AR/VR application uses an RGB camera to capture a monocularimage of a user's real-world face. The AR/VR application applies varioustrained machine learning techniques on the captured image of the faceand obtains a depth map and to apply one or more visual effects to thecaptured image. In some implementations, the AR/VR applicationcontinuously captures images of the user's face in real time orperiodically to continuously or periodically update the applied one ormore visual effects (e.g., the augmented reality eyewear or hat). Thisallows the user to move around in the real world and see the one or morevisual effects update in real time.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, is described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes augmented reality content items (e.g., corresponding toapplying augmented reality experiences). An augmented reality contentitem or augmented reality item may be a real-time special effect andsound that may be added to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsthat refer to modifications that may be applied to image data (e.g.,videos or images). This includes real-time modifications, which modifyan image as it is captured using device sensors (e.g., one or multiplecameras) of a client device 102 and then displayed on a screen of theclient device 102 with the modifications. This also includesmodifications to stored content, such as video clips in a gallery thatmay be modified. For example, in a client device 102 with access tomultiple augmented reality content items, a user can use a single videoclip with multiple augmented reality content items to see how thedifferent augmented reality content items will modify the stored clip.For example, multiple augmented reality content items that applydifferent pseudorandom movement models can be applied to the samecontent by selecting different augmented reality content items for thecontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by sensors of a client device 102 would modify the captureddata. Such data may simply be displayed on the screen and not stored inmemory, or the content captured by the device sensors may be recordedand stored in memory with or without the modifications (or both). Insome systems, a preview feature can show how different augmented realitycontent items will look within different windows in a display at thesame time. This can, for example, enable multiple windows with differentpseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements, characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification, properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

True Size Estimation System

FIG. 5 is a block diagram showing an example true size estimation system224, according to example examples. True size estimation system 224includes a set of components 510 that operate on a set of input data(e.g., a monocular image depicting a face of a user 501, depth map data502, and eyewear data 503). True size estimation system 224 includes alandmark generation module 512, a landmark selection module 514, a depthmap module 517, a real-world scale computation module 516, an imagemodification module 518, an eyewear generation module 519, and an imagedisplay module 520. All or some of the components of the true sizeestimation system 224 can be implemented by a server, in which case, themonocular image depicting a face of the user 501 and the depth map data502 are provided to the server by the client device 102. In some cases,some or all of the components of the true size estimation system 224 canbe implemented by the client device 102.

The landmark generation module 512 receives a monocular image depictinga face of a user 501. This image can be received as part of a real-timevideo stream, a previously captured video stream or a new image capturedby a camera of the client device 102. The landmark generation module 512applies one or more machine learning techniques to identify and segmenta face of the user from the background of the monocular image depictingthe face of the user 501. The landmark generation module 512 can thenapply one or more machine learning techniques to identify one or morelandmarks on the identified face. For example, as shown in FIG. 6 , thelandmark generation module 512 can generate an output image 610 in whicha plurality of landmarks 616, 612 and 614 are provided. Each landmarkuniquely and specifically identifies a region of the face, such as theeyebrows, the eyes, the nose, temple, the nose bridge, the mouth, thenose, the ears, the hair, the cheeks, the forehead, and so forth.

The landmark generation module 512 can compute visibility and stabilityparameters for each of the identified landmarks. The visibilityparameter specifies a score, rank or amount of the given landmark thatis visible in the monocular image depicting a face of a user 501. In anexample, the landmark generation module 512 computes the visibilityparameter by retrieving a generic three-dimensional (3D) facial modelrepresentation. The landmark generation module 512 identifies a set oflandmarks on the 3D facial model representation. The landmark generationmodule 512 selects a given landmark from the identified landmarks of theuser's face and matches the given landmark to a corresponding landmarkon the 3D facial model. For example, the landmark generation module 512selects the ears landmark and matches the ears landmark to the earsportion of the 3D facial model. The landmark generation module 512computes how much of the ears landmark matches the ears portion of the3D facial model to determine a level of visibility or visibilityparameter. In an implementation, if 60 percent of the ears landmarkmatches the ears portion, the visibility parameter is set to a score of60 percent. Namely, the visibility parameter is proportional to theamount of overlap or amount by which the landmark matches thecorresponding portion of the 3D facial model.

The landmark generation module 512 continues to compute the visibilityparameter for the rest of the landmarks that are identified in thereceived image in a similar way using the 3D facial model. Afterobtaining the visibility parameter for each of the landmarks identifiedin the face, the landmark generation module 512 determines thevisibility parameter of each landmark as a function of a number of theremaining set of landmarks that match the generic 3D facial modelrepresentation. Specifically, the landmark generation module 512computes how many of the landmarks have a visibility score that isgreater than a certain threshold value (e.g., greater than 75 percent).The landmark generation module 512 then computes an overall visibilityparameter for the identified landmarks based on the number of landmarksthat have the visibility score that is greater than the threshold value.The greater the number of landmarks with the visibility score greaterthan the threshold value, the greater the overall visibility parameter.

The landmark generation module 512 can also compute a stabilityparameter for each of the identified landmarks. The stability parameterindicates how much each given landmark moves over a threshold number offrames. For example, the landmark generation module 512 can receive asequence of frames of a video that include the monocular image depictinga face of a user 501. After identifying the landmarks on the face, thelandmark generation module 512 can determine the 3D or 2D positions ofthe landmarks in the image. The landmark generation module 512 detectschanges to the 3D or 2D positions of each landmark. Based on the amountof movement or changes to the 3D or 2D positions, the landmarkgeneration module 512 generates and computes a corresponding stabilityparameter for the given landmark. Landmarks that have less amount ofmovement are assigned a greater stability parameter score than thosethat are determined to move more than a threshold amount per thresholdnumber of frames (e.g., over 40 frames).

In some cases, the landmark generation module 512 computes the stabilityparameter of each of the landmarks based on a frame rate associated withthe client device 102 used to capture the video that depicts the face ofthe user. Namely, a client device 102 that has a first frame rate can beassociated with a greater number of frames over which the stabilityparameter is computed than another client device with a lower secondframe rate. Namely, the threshold number of frames can vary based on theframe rate of the client device 102. After computing the stability andvisibility parameters of each of the landmarks, the landmark generationmodule 512 sorts and ranks the landmarks based on their respectivestability and visibility parameters.

In some cases, prior to performing the visibility and stabilityparameter computations, the landmark generation module 512 can remove aset of interfering landmarks from the identified set of landmarks of theface. Namely, the landmark generation module 512 can access apredetermined list of known or predetermined set of interferinglandmarks (e.g., a hair region, a facial garment (e.g., a face mask oreyeglasses), a neck region). In response to determining that one of theidentified landmarks corresponds or is included among the predeterminedset of interfering landmarks, the landmark generation module 512 canremove or discard or not consider such an identified landmark. Afterremoving the set of interfering landmarks from the plurality oflandmarks, the landmark generation module 512 provides a resulting setof remaining landmarks of the total identified plurality of landmarks tothe landmark selection module 514. In some cases, the removal of theinterfering landmarks can be performed after computing the visibilityand stability parameters of all of the identified plurality of landmarkson the face and before the top landmarks are selected. This way, eventhough a given landmark (e.g., a facial garment) is associated with ahighest visibility and stability parameter than all other landmarks(e.g., the eyes and nose), the given landmark is not included orselected among the top landmarks by the landmark selection module 514because it is considered to be an interfering landmark.

The landmark generation module 512 provides the identified landmarks andtheir respective visibility and stability parameters to the landmarkselection module 514. The landmark selection module 514 selects a set oftop landmarks that are associated with a greater visibility and greaterstability parameters than a remaining set of landmarks. In some cases,the landmark selection module 514 can receive an indication of anaugmented reality element that is to be included in the image thatdepicts the user's face. Based on the type of augmented reality element(e.g., eyewear augmented reality element), the landmark generationmodule 512 obtains a threshold quantity or number of landmarks that areto be selected as the top landmarks. For example, if the eyewearaugmented reality element is selected, the threshold number of toplandmarks includes two top landmarks that need to be selected. In thiscase, the landmark selection module 514 obtains and tracks the twolandmarks that are ranked higher than all other landmarks that areidentified based on their respective visibility and stability parameters(or metrics). The threshold number of top landmarks can also bespecified by the user.

In some implementations, the landmark selection module 514 selects thetop landmarks at random. For example, for a first sequence of frames ora first duration of a video, the landmark selection module 514 canselects a first set of landmarks (e.g., eyes and nose) that havevisibility and stability parameters that exceed a specified value. Then,for a second sequence of frames or a second duration of the video, thelandmark selection module 514 can selects a second set of landmarks(e.g., eyes and ears) that have visibility and stability parameters thatexceed the specified value. Namely, the landmark selection module 514can identify a collection of landmarks (e.g., more than the thresholdnumber of top landmarks) which have visibility and stability parametersthat are greater than a specified value or that satisfy a criterion orcriteria. The landmark selection module 514 can then alternate and varyrandomly which subset of the collection of landmarks (e.g., which two ofthe multiple collection of landmarks) to include in the threshold numberof top landmarks.

In an implementation, based on tracking the two landmarks, the landmarkselection module 514 can provide the information pertaining to the twolandmarks to the real-world scale computation module 516 to compute thereal-world scale of the user depicted in the image. In this way, thereal-world scale computation module 516 can compute a real-world scaleof a user in a video or image based on a first set of landmarks at afirst point in time (e.g., during a first portion of a video) and cancompute the real-world scale of the user in the video or image based ona different second set of landmarks at a second point in time. Theoperations for identifying the facial landmarks, removing certainlandmarks, and computing the stability and visibility parameters of thefacial landmarks are repeated for each video frame or each subset ofvideo frames to update the stability and visibility parameters. Theupdated stability and visibility parameters are provided to the landmarkselection module 514 to update the landmarks selected as the toplandmarks and iteratively correct the real-world scale of the face basedon the updated plurality of landmarks.

The depth map module 517 receives depth map data 502 from a depth sensoror depth camera of the client device 102. The depth map data 502 isassociated with the image or video being processed by the landmarkgeneration module 512 and the landmark selection module 514. The depthmap module 517 receives a face segmentation mask for the face depictedin the monocular image depicting the face of the user 501, such as fromthe landmark generation module 512. The depth map module 517 appliesmomentum smoothing to the depth map data 502 based on the facesegmentation mask. The depth map module 517 provides the smoothed depthmap to the real-world scale computation module 516. The real-world scalecomputation module 516 computes the real-world scale of the user's facedepicted in the image based on the top landmarks provided by thelandmark selection module 514 and the smoothed depth map received fromthe depth map module 517. For example, the real-world scale computationmodule 516 can determine a distance to a given landmark from the clientdevice 102 based on the smoothed depth map. Based on the distance, thereal-world scale computation module 516 can compute the real-worldfacial measurements of the landmarks identified in the image receivedfrom the client device 102. Namely, the real-world scale computationmodule 516 can use heuristics or machine learning techniques to computethe real-world physical size and measurements of the user's face when alandmark appears at a certain size and is at a certain distance awayfrom the client device 102. Using this information, the real-world scalecomputation module 516 can determine a scale that transforms thereal-world physical measurements to the sizes of the landmarks and facedepicted in the image captured by the client device 102.

The real-world scale computation module 516 provides the scale to theimage modification module 518 and the eyewear generation module 519. Theeyewear generation module 519 obtains an augmented reality graphicalelement that includes augmented reality eyewear. In an example, thereal-world scale computation module 516 receives eyewear data 503. Theeyewear data 503 defines the physical material and rigid properties ofphysical sunglasses or eyeglasses. The eyewear data 503 also includesthe physical measurements of the physical sunglasses or eyeglasses. Theeyewear data 503 also provides an augmented reality element having asize and dimensions and behavior (deformation properties) thatrepresents the physical sunglasses or eyeglasses.

The eyewear data 503 can include information from a physical glassesdesigner that specifies physical attributes of the glasses, such asphysical measurements, a style of the physical glasses, a lens shape andcolor, and a frame style and color.

The eyewear generation module 519 can increase or decrease a size of theaugmented reality element based on the real-world scale of the user'sface provided by the real-world scale computation module 516. Forexample, if the user's face in the image is at a first distance to theclient device 102, the scale can be determined based on the landmarksand smoothed depth data to be a first value. The eyewear generationmodule 519, in this case, adjusts the size of the augmented realityelement based on the first value. If the user's face is moved furtheraway from the client device 102, the face becomes smaller and is at afarther second distance. In this case, the scale can be determined to bea second smaller value than the first. In this case, the eyeweargeneration module 519 reduces the size of the augmented reality elementbased on the second value to be smaller.

The eyewear generation module 519 provides the augmented reality elementhaving the adjusted scale to the image modification module 518. Theimage modification module 518 positions the scaled augmented realityelement on the face of the user depicted in the image or video. In somecases, the image modification module 518 identifies a nose bridgelandmark based on the output of the landmark generation module 512. Theimage modification module 518 then identifies a nose bridge portion ofthe augmented reality element and centers the nose bridge portion of theaugmented reality element on top of the nose bridge landmark. In someimplementations, the image modification module 518 identifies a startpoint and end point of the nose bridge landmark. The image modificationmodule 518 selects a point between the start and end points of the nosebridge landmark over which to position the nose bridge portion of theaugmented reality element. The point between the start and end pointscan be heuristically determined, set by a user, or learned by a machinelearning technique. In some cases, the image modification module 518initially places the augmented reality element at a midpoint between thestart and end points. For example, the image modification module 518 candetermine a topology of the face of the user based on the landmarksgenerated by the landmark generation module 512. The image modificationmodule 518 can then position the scaled augmented reality graphicalelement within the image or video based on the topology of the user'sface. In an example, the image modification module 518 positions a nosebridge portion of the augmented reality graphical element apredetermined distance above a nose bridge landmark within the topology.

For example, as shown in FIG. 7 , the image modification module 518places the augmented reality eyewear 720 on the image 710 depicting theuser's face. The nose bridge portion 722 of the augmented realityeyewear 720 is placed and positioned over the nose bridge landmark ofthe user's face.

The image modification module 518 can detect a user's finger in thevideo stream received from the client device. The image modificationmodule 518 can determine that the user's finger overlaps the nose bridgeportion of the augmented reality element. The image modification module518 can determine then that the finger is moved vertically towards theeyebrows. In response, the image modification module 518 adjusts thepoint at which the augmented reality element is positioned on the nosebridge portion by a specified amount to be closer to the eyebrowsdepending on the amount of movement of the finger. The imagemodification module 518 can determine then that the finger is movedvertically towards the mouth. In response, the image modification module518 adjusts the point at which the augmented reality element ispositioned on the nose bridge portion by a specified amount to befurther from the eyebrows and closer to the mouth depending on theamount of movement of the finger. The image modification module 518 canrecord or store the position along the nose bridge landmark over whichthe nose bridge portion is placed. The image modification module 518 canplace subsequent or other eyewear augmented reality elements such thattheir respective nose bridge portions are positioned at the storedposition on the nose bridge landmark.

The image modification module 518 can adjust the image captured by thecamera and based on the output of the eyewear generation module 519. Theimage modification module 518 adjusts the way in which the augmentedreality eyeglasses or hat is positioned on a user(s) depicted in animage. Image display module 520 combines the adjustments made by theimage modification module 518 into the received monocular imagedepicting the user's face. The image is provided by the image displaymodule 520 to the client device 102 and can then be sent to another useror stored for later access and display.

In an example, the eyewear generation module 519 can recommend orautomatically select an augmented reality eyewear element to display onthe user's face based on fit factors computed for a plurality ofaugmented reality eyewear elements. For example, the eyewear generationmodule 519 can receive, as part of the eyewear data 503, a plurality ofphysical measurements of a plurality of physical glasses. The physicalglasses can be associated with a particular manufacturer or can be aspecified set of popular glasses or can include all of the physicalglasses that have associated eyewear data 503. The eyewear generationmodule 519 can select a subset of physical glasses for which to computethe fit factor based on a style and one or more attributes associatedwith the user, such as an age of the user, a gender of the user,preferences of the user, and the computed real-world scale of the face.Namely, the eyewear generation module 519 can determine that thephysical size of the user's face is of a specified value and can selecta subset of physical glasses that match the specified value. In anotherexample, the eyewear generation module 519 can determine that the userin the image is a child and can select a set of child friendly glassesfor which to compute the fit factor.

The eyewear generation module 519 computes a fit factor for each of thephysical glasses based on the physical measurements of the glassesprovided by the eyewear data 503 and the real-world scale of the face ofthe user. In an implementation, the eyewear generation module 519computes a fit factor for each of the glasses by determining a firstdistance between a nose bridge portion of each one of the plurality ofphysical glasses and a temples portion of the respective one of theplurality of physical glasses.

The eyewear generation module 519 computes a second distance in thecomputed real-world scale of the face of the user between a nose bridgeand a cheek bone or temple of the face of the user. Namely, the eyeweargeneration module 519 determines the real-world physical measurements ofthe landmarks depicted in the image of the user's face and the smootheddepth map and computes the distance between the nose bridge landmark anda cheek bone or temple landmark of the physical measurements of the faceof the user. The eyewear generation module 519 then computes the fitfactor for each of the glasses as a function of the first distance andthe second distance. In an example, the fit factor represents adifference between the first and second distances, such that a smallerfit factor represents a better fit for the user. In some cases, thevalue for the fit factor is inversely related to the difference betweenthe first and second distances, such that a greater fit factorrepresents a better fit for the user. Namely, the eyewear generationmodule 519 computes the fit factor for a given one of the glasses basedon how close the distance between nose bridge portion of the givenglasses and a temples portion of the given glasses is to the distancebetween the nose bridge landmark and a cheek bone or temple landmark ofthe user's face. The eyewear generation module 519 ranks and sorts allof the physical glasses based on the computed fit factor andautomatically selects one or a subset of the physical glasses having atop fit factor (e.g., a fit factor better than the rest) to present tothe user.

As another example, the eyewear generation module 519 computes a fitfactor for each of the glasses by determining lens dimensions of eachone of the plurality of physical glasses. For example, as shown in FIG.8 , the eyewear generation module 519 determines the lens dimension 830of the augmented reality glasses 820. The lens dimension 830 shown inthe image 810 of FIG. 8 is generated by scaling the lens dimension ofthe physical glasses based on the real-world scaling factor of theuser's face. Namely, the lens dimension 830 is adjusted in size by anamount determined based on the real-world scaling factor of the user'sface before being placed on the image 810 depicting the user's face. Theeyewear generation module 519 determines the real-world physicalmeasurements of the landmarks depicted in the image of the user's faceand the smoothed depth map and then computes the fit factor for each ofthe glasses as a function of the lens dimensions and the real-worldphysical measurements of the landmarks. The eyewear generation module519 ranks and sorts all of the physical glasses based on the computedfit factor and automatically selects one or a subset of the physicalglasses having a top fit factor (e.g., a fit factor better than therest) to present to the user.

In some examples, the eyewear generation module 519 displays a warningmessage in response to determining that a fit factor for each of aplurality of physical glasses fails to be satisfied. For example, if thefacial dimensions of the user exceed a measurement (e.g., are too smallor too large) of each of the glasses (e.g., the distance between thenose bridge and temple on the user's face is larger by a specifiedamount than a distance between a nose bridge portion and temple of theglasses), the eyewear generation module 519 displays the warningmessage.

In some examples, the eyewear generation module 519 displays a promptshown in FIG. 9 that lists the top ranked glasses. Namely, the eyeweargeneration module 519 selects a specified number of glasses (e.g., threeglasses) that have an associated fit factor that is better than fitfactors associated with a remaining set of glasses. The eyeweargeneration module 519 can receive a user selection of physical glassesamong the listed set of physical glasses. In response, the eyeweargeneration module 519 scales the augmented reality element representingthe physical glasses selected by the user and positions the scaledaugmented reality element within the image or video feed depicting theuser's face.

In some implementations, the image modification module 518 can deformthe augmented reality element placed on top of the user's face in theimage or video based on movement of the user's face. For example, theimage modification module 518 can obtain rigid material propertiesassociated with the augmented reality graphical element, such as byaccessing the eyewear data 503. The image modification module 518deforms a first portion of the augmented reality graphical element inresponse to determining that the rigid material properties correspond toa first rigidity amount. The image modification module 518 deforms aplurality of portions of the augmented reality graphical element inresponse to determining that the rigid material properties correspond toa second rigidity amount. In some cases, the first rigidity amount issmaller than the second rigidity amount. For example, if the glasses aremore rigid, the image modification module 518 can bend or deform thenose bridge portion of the augmented reality element and the templesportion of the augmented reality element when positioned on the user'sface relative to the default formation of the augmented reality elementwhen not placed on the user's face. As another example, if the glassesare more flexible, the image modification module 518 can bend or deformonly the temples portion of the augmented reality element whenpositioned on the user's face relative to the default formation of theaugmented reality element when not placed on the user's face.

FIG. 10A is a flowchart of a process 1000, in accordance with someexample examples. Although the flowchart can describe the operations asa sequential process, many of the operations can be performed inparallel or concurrently. In addition, the order of the operations maybe re-arranged. A process is terminated when its operations arecompleted. A process may correspond to a method, a procedure, and thelike. The steps of methods may be performed in whole or in part, may beperformed in conjunction with some or all of the steps in other methods,and may be performed by any number of different systems or any portionthereof, such as a processor included in any of the systems.

At operation 1001, a client device 102 receives an image that includes adepiction of a face of a user, as discussed above. For example, the truesize estimation system 224 can capture an image that depicts one or morefaces of one or more users (e.g., a plurality of users).

At operation 1002, the client device 102 generates a plurality oflandmarks of the face based on the received image, as discussed above.As an example, the true size estimation system 224 can generate thelandmarks by applying a machine learning technique to the image.

At operation 1003, the client device 102 removes a set of interferinglandmarks from the plurality of landmarks resulting in a remaining setof landmarks of the plurality of landmarks, as discussed above.

At operation 1004, the client device 102 obtains a depth map for theface of the user, as discussed above.

At operation 1005, the client device 102 computes a real-world scale ofthe face of the user based on the depth map and the remaining set oflandmarks, as discussed above.

FIG. 10B is a flowchart of a process 1010, in accordance with someexample examples. Although the flowchart can describe the operations asa sequential process, many of the operations can be performed inparallel or concurrently. In addition, the order of the operations maybe re-arranged. A process is terminated when its operations arecompleted. A process may correspond to a method, a procedure, and thelike. The steps of methods may be performed in whole or in part, may beperformed in conjunction with some or all of the steps in other methods,and may be performed by any number of different systems or any portionthereof, such as a processor included in any of the systems.

At operation 1011, a client device 102 receives an image that includes adepiction of a face of a user, as discussed above.

At operation 1012, the client device 102 computes a real-world scale ofthe face of the user based on a selected subset of landmarks of the faceof the user, as discussed above.

At operation 1013, the client device 102 obtains an augmented realitygraphical element comprising augmented reality eyewear, as discussedabove.

At operation 1014, the client device 102 scales the augmented realitygraphical element based on the computed real-world scale of the face, asdiscussed above.

At operation 1015, the client device 102 positions the scaled augmentedreality graphical element within the image on the face of the user.

Machine Architecture

FIG. 11 is a diagrammatic representation of the machine 1100 withinwhich instructions 1108 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1100to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1108 may cause the machine 1100to execute any one or more of the methods described herein. Theinstructions 1108 transform the general, non-programmed machine 1100into a particular machine 1100 programmed to carry out the described andillustrated functions in the manner described. The machine 1100 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1100 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1100 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1108, sequentially or otherwise,that specify actions to be taken by the machine 1100. Further, whileonly a single machine 1100 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1108 to perform any one or more of themethodologies discussed herein. The machine 1100, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1100 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1100 may include processors 1102, memory 1104, andinput/output (I/O) components 1138, which may be configured tocommunicate with each other via a bus 1140. In an example, theprocessors 1102 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1106 and a processor 1110 that execute the instructions 1108.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 11 shows multiple processors 1102, the machine 1100 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1104 includes a main memory 1112, a static memory 1114, and astorage unit 1116, all accessible to the processors 1102 via the bus1140. The main memory 1104, the static memory 1114, and the storage unit1116 store the instructions 1108 embodying any one or more of themethodologies or functions described herein. The instructions 1108 mayalso reside, completely or partially, within the main memory 1112,within the static memory 1114, within machine-readable medium 1118within the storage unit 1116, within at least one of the processors 1102(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1100.

The I/O components 1138 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1138 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1138 mayinclude many other components that are not shown in FIG. 11 . In variousexamples, the I/O components 1138 may include user output components1124 and user input components 1126. The user output components 1124 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1126 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1138 may include biometriccomponents 1128, motion components 1130, environmental components 1132,or position components 1134, among a wide array of other components. Forexample, the biometric components 1128 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1130 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1132 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1134 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1138 further include communication components 1136operable to couple the machine 1100 to a network 1120 or devices 1122via respective coupling or connections. For example, the communicationcomponents 1136 may include a network interface component or anothersuitable device to interface with the network 1120. In further examples,the communication components 1136 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), WiFi® components, and othercommunication components to provide communication via other modalities.The devices 1122 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1136 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1136 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1136, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1112, static memory 1114, andmemory of the processors 1102) and storage unit 1116 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1108), when executedby processors 1102, cause various operations to implement the disclosedexamples.

The instructions 1108 may be transmitted or received over the network1120, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1136) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 1108may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 1122.

Software Architecture

FIG. 12 is a block diagram 1200 illustrating a software architecture1204, which can be installed on any one or more of the devices describedherein. The software architecture 1204 is supported by hardware such asa machine 1202 that includes processors 1220, memory 1226, and I/Ocomponents 1238. In this example, the software architecture 1204 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1204 includes layerssuch as an operating system 1212, libraries 1210, frameworks 1208, andapplications 1206. Operationally, the applications 1206 invoke API calls1250 through the software stack and receive messages 1252 in response tothe API calls 1250.

The operating system 1212 manages hardware resources and provides commonservices. The operating system 1212 includes, for example, a kernel1214, services 1216, and drivers 1222. The kernel 1214 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1214 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1216 canprovide other common services for the other software layers. The drivers1222 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1222 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1210 provide a common low-level infrastructure used by theapplications 1206. The libraries 1210 can include system libraries 1218(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1210 can include APIlibraries 1224 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1210 can also include a widevariety of other libraries 1228 to provide many other APIs to theapplications 1206.

The frameworks 1208 provide a common high-level infrastructure that isused by the applications 1206. For example, the frameworks 1208 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1208 canprovide a broad spectrum of other APIs that can be used by theapplications 1206, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1206 may include a home application1236, a contacts application 1230, a browser application 1232, a bookreader application 1234, a location application 1242, a mediaapplication 1244, a messaging application 1246, a game application 1248,and a broad assortment of other applications such as a externalapplication 1240. The applications 1206 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1206, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1240 (e.g., an application developed using the ANDROID™ or IOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the external application 1240can invoke the API calls 1250 provided by the operating system 1212 tofacilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1102 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: removing, from an objectdepicted in an image, a set of interfering landmarks from a plurality oflandmarks resulting in a remaining set of landmarks of the plurality oflandmarks; determining a stability parameter as a function of movementof the remaining set of landmarks over a plurality of frames of a videocomprising the image; and computing a real-world scale of the objectdepicted in the image based on the stability parameter and the remainingset of landmarks.
 2. The method of claim 1, the object comprising a faceof a user, further comprising: generating the plurality of landmarks ofthe face based on the image; and obtaining a depth map for the face ofthe user, the real-world scale of the face being computed based on thedepth map.
 3. The method of claim 1, further comprising: sorting theremaining set of landmarks based on a visibility parameter correspondingto visibility of the plurality of landmarks and the stability parameter.4. The method of claim 3, further comprising: obtaining a genericthree-dimensional object representation; matching the remaining set oflandmarks to the generic three-dimensional object representation; anddetermining the visibility parameter as a function of a number of theremaining set of landmarks that match the generic three-dimensionalobject representation.
 5. The method of claim 3, further comprising:tracking movement of the remaining set of landmarks over the pluralityof frames.
 6. The method of claim 1, wherein the set of interferinglandmarks comprises at least one of a hair region, one or more facialgarments, or a face mask.
 7. The method of claim 1, further comprisingapplying a machine learning model to the image to identify the pluralityof landmarks.
 8. The method of claim 1, further comprising: selecting athreshold number of top landmarks from the remaining set of landmarksbased on visibility or stability parameters associated with each of theremaining set of landmarks, wherein the real-world scale of the objectis computed based on the selected threshold of top landmarks.
 9. Themethod of claim 8, wherein the threshold number comprises two toplandmarks.
 10. The method of claim 8, further comprising: selecting eyesand nose landmarks as the top landmarks in response to determining thatthe eyes and nose landmarks are associated with greater visibility andstability parameters than other landmarks in the remaining set oflandmarks.
 11. The method of claim 8, wherein the top landmarks areselected at random from the remaining set of landmarks.
 12. The methodof claim 11, wherein a first set of top landmarks are selected for afirst subset of the frames, and wherein a second set of top landmarksare selected for a second subset of frames.
 13. The method of claim 1,further comprising: generating an object segmentation mask for theobject depicted in the image; and applying momentum smoothing to a depthmap based on the object segmentation mask.
 14. The method of claim 1,further comprising: updating the plurality of landmarks as each frame ofthe video depicting the object is received; and iteratively correctingthe real-world scale of the object based on the updated plurality oflandmarks by repeating the removing and obtaining operations for theupdated plurality of landmarks.
 15. The method of claim 1, furthercomprising: obtaining an augmented reality graphical element comprisingaugmented reality eyewear; identifying a nose bridge landmark based onthe remaining set of landmarks; and positioning the augmented realitygraphical element within the image on the object based on the nosebridge landmark.
 16. The method of claim 15, further comprising:positioning a nose bridge portion of the augmented reality graphicalelement a predetermined distance above the nose bridge landmark.
 17. Themethod of claim 15, further comprising: adjusting a scale of theaugmented reality graphical element based on the computed real-worldscale of the object.
 18. The method of claim 1, further comprising:computing a distance between the remaining set of landmarks; retrievinga measure of depth for the remaining set of landmarks; and generating ascaling factor based on the distance and the measured depth that relatesa size of the object in the image to a real-world size of the object,wherein a size of an augmented reality graphical element is modified asa function of the scaling factor.
 19. A system comprising: a processor;and a memory component having instructions stored thereon, when executedby the processor, causes the processor to perform operations comprising:removing, from an object depicted in an image, a set of interferinglandmarks from a plurality of landmarks resulting in a remaining set oflandmarks of the plurality of landmarks; determining a stabilityparameter as a function of movement of the remaining set of landmarksover a plurality of frames of a video comprising the image; andcomputing a real-world scale of the object depicted in the image basedon the stability parameter and the remaining set of landmarks.
 20. Anon-transitory computer-readable storage medium having stored thereon,instructions when executed by a processor, causes the processor toperform operations comprising: removing, from an object depicted in animage, a set of interfering landmarks from a plurality of landmarksresulting in a remaining set of landmarks of the plurality of landmarks;determining a stability parameter as a function of movement of theremaining set of landmarks over a plurality of frames of a videocomprising the image; and computing a real-world scale of the objectdepicted in the image based on the stability parameter and the remainingset of landmarks.